Searching the Veterinary Literature: A Comparison of the Coverage of Veterinary Journals by Nine Bibliographic Databases
Bibliographic record
Abstract
A thorough search of the literature to find the best evidence is central to the practice of evidence-based veterinary medicine. This requires knowing which databases to search to maximize journal coverage. The aim of the present study was to compare the coverage of active veterinary journals by nine bibliographic databases to inform future systematic reviews and other evidence-based searches. Coverage was assessed using lists of included journals produced by the database providers. For 121 active veterinary journals in the "Basic List of Veterinary Medical Serials, Third Edition," the percentage coverage was the highest for Scopus (98.3%) and CAB Abstracts (97.5%). For an extensive list of 1,139 journals with significant veterinary content compiled from a variety of sources, coverage was much greater in CAB Abstracts (90.2%) than in any other database, the next highest coverage being in Scopus (58.3%). The maximum coverage of the extensive journal list that could be obtained in a search without including CAB Abstracts was 69.8%. It was concluded that to maximize journal coverage and avoid missing potentially relevant evidence, CAB Abstracts should be included in any veterinary literature search.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.070 | 0.067 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".